Automated Segmentation for Connectomics Utilizing Higher-Order Biological Priors

This thesis presents novel methodological approaches for the automated segmentation of neurons from electron microscopic image volumes using machine learning techniques. New potentials for neural segmentation are revealed by incorporating (high-level) biological prior knowledge. This goes beyond the modeling of neural tissue which has been applied for the purpose of its segmentation, so far. Firstly, the V-Multicut algorithm is introduced which enables the consideration of topological constraints for segmented membranes. In this way, biologically implausible appearances of membranes are corrected. Secondly, this thesis proves that, in addition to local evidence and topological requirements for the detection of neural membranes, the consideration of high-level biological prior knowledge is beneficial. For this task, both the recently proposed Asymmetric Multiway Cut and the introduced Semantic Agglomerative Clustering algorithm are implemented and quantitatively evaluated. To be precise, the spatial separation of dendrites and axons in mammals is exploited to significantly improve the segmentation quality. Additionally, new ways to improve the scalability of the used algorithms are presented. All in all this thesis serves as another step towards fully automated segmentation of neurons and contributes to the field of connectomics

Deep Space Gateway Concept Science Workshop : February 27–March 1, 2018, Denver, Colorado

The purpose of this workshop is to discuss what science could be leveraged from a deep space gateway, as well as first-order determination of what instruments are required to acquire the scientific data.Institutional Support, National Aeronautics and Space Administration, Lunar and Planetary Institute, Universities Space Research Association ; Executive Committee, Ben Bussey, HEOMD Chief Scientist, NASA Headquarters, Jim Garvin, Goddard Space Flight Center Chief Scientist, Michael New, NASA Headquarters, Deputy AA for Research, SMD, Paul Niles, Executive Secretary, NASA Johnson Space Center, Jim Spann, MSFC Chief Scientist, Eileen Stansbery, Johnson Space CenterPARTIAL CONTENTS: Deep Space Gateway as a Deployment Staging Platform and Communication Hub of Lunar Heat Flow Experiment--Lunar Seismology Enabled by a Deep Space Gateway--In-Situ Measurements of Electrostatic Dust Transport on the Lunar Surface--Science Investigations Enabled by Magnetic Field Measurements on the Lunar Surface--Enhancing Return from Lunar Surface Missions via the Deep Space Gateway--Deep Space Gateway Support of Lunar Surface Ops and Tele-Operational Transfer of Surface Assets to the Next Landing Site--Development of a Lunar Surface Architecture Using the Deep Space Gateway--The Deep Space Gateway: The Next Stepping Stone to Mar

Recent Development of Hybrid Renewable Energy Systems

Abstract: The use of renewable energies continues to increase. However, the energy obtained from renewable resources is variable over time. The amount of energy produced from the renewable energy sources (RES) over time depends on the meteorological conditions of the region chosen, the season, the relief, etc. So, variable power and nonguaranteed energy produced by renewable sources implies intermittence of the grid. The key lies in supply sources integrated to a hybrid system (HS)